This paper tries to shed some light on the mutual influence of citizen behaviour and the spread of a virus in an epidemic. While the spread of a virus from infectious to susceptible persons and the outbreak of an infection leading to more or less severe illness and, finally, to recovery and immunity or death has been modelled with different kinds of models in the past, the influence of certain behaviours to keep the epidemic low and to follow recommendations of others to apply these behaviours has rarely been modelled. The model introduced here uses a theory of the effect of norm invocations among persons to find out the effect of spreading norms interacts with the progress of an epidemic. Results show that norm invocations matter. The model replicates the histories of the COVID-19 epidemic in various region, including “second waves” (but only until the end of 2021 as afterwards the official statistics ceased to be reliable as many infected persons did not report their positive test results after countermeasures were relieved), and shows that the calculation of the reproduction numbers from current reported infections usually overestimates the “real” but in practice unobservable reproduction number.
This is the third (and last) release. Due to the fact that the real world data used for calibration up to the end of 2021 were no longer reliable as not all Covid infection data could be collected as many infected persons did not report their test results and the German RKI did no longer report data over weekends, validation and calibration were no longer possible to improve the code. The model runs now on NetLogo 6.2. This is the main change as compared to earlier versions. An additional effect is that the model calculates not only the basic reproduction number but also a dispersion parameter in a version described in detail in the extended ODD description. Another new feature is that vaccination can be switched on (starting about the end of the first year, and with different scales of progress.